{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from FcaNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = fcanet(drop = 0.1).to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.1168 Top-1 准确率: 0.0788 Top-5 准确率: 0.2440\n",
      "val 损失: 4.1807 Top-1 准确率: 0.0714 Top-5 准确率: 0.2284\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.6171 Top-1 准确率: 0.1552 Top-5 准确率: 0.3983\n",
      "val 损失: 3.4607 Top-1 准确率: 0.1642 Top-5 准确率: 0.4279\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2258 Top-1 准确率: 0.2243 Top-5 准确率: 0.5025\n",
      "val 损失: 3.1639 Top-1 准确率: 0.2154 Top-5 准确率: 0.5153\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9177 Top-1 准确率: 0.2891 Top-5 准确率: 0.5814\n",
      "val 损失: 2.9359 Top-1 准确率: 0.2652 Top-5 准确率: 0.5596\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6940 Top-1 准确率: 0.3375 Top-5 准确率: 0.6271\n",
      "val 损失: 2.9653 Top-1 准确率: 0.2708 Top-5 准确率: 0.5706\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5182 Top-1 准确率: 0.3727 Top-5 准确率: 0.6657\n",
      "val 损失: 2.5695 Top-1 准确率: 0.3448 Top-5 准确率: 0.6558\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3566 Top-1 准确率: 0.4107 Top-5 准确率: 0.6977\n",
      "val 损失: 2.3817 Top-1 准确率: 0.3761 Top-5 准确率: 0.6941\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2443 Top-1 准确率: 0.4348 Top-5 准确率: 0.7213\n",
      "val 损失: 2.1202 Top-1 准确率: 0.4308 Top-5 准确率: 0.7482\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1360 Top-1 准确率: 0.4600 Top-5 准确率: 0.7385\n",
      "val 损失: 2.0601 Top-1 准确率: 0.4559 Top-5 准确率: 0.7528\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0514 Top-1 准确率: 0.4782 Top-5 准确率: 0.7570\n",
      "val 损失: 1.9535 Top-1 准确率: 0.4699 Top-5 准确率: 0.7776\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9588 Top-1 准确率: 0.5016 Top-5 准确率: 0.7720\n",
      "val 损失: 1.9960 Top-1 准确率: 0.4676 Top-5 准确率: 0.7754\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8956 Top-1 准确率: 0.5148 Top-5 准确率: 0.7825\n",
      "val 损失: 1.8094 Top-1 准确率: 0.5115 Top-5 准确率: 0.8047\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8138 Top-1 准确率: 0.5372 Top-5 准确率: 0.7987\n",
      "val 损失: 1.7690 Top-1 准确率: 0.5190 Top-5 准确率: 0.8088\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7501 Top-1 准确率: 0.5489 Top-5 准确率: 0.8095\n",
      "val 损失: 1.7238 Top-1 准确率: 0.5291 Top-5 准确率: 0.8151\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6901 Top-1 准确率: 0.5655 Top-5 准确率: 0.8190\n",
      "val 损失: 1.7815 Top-1 准确率: 0.5155 Top-5 准确率: 0.8160\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6380 Top-1 准确率: 0.5782 Top-5 准确率: 0.8273\n",
      "val 损失: 1.5131 Top-1 准确率: 0.5748 Top-5 准确率: 0.8594\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5917 Top-1 准确率: 0.5880 Top-5 准确率: 0.8373\n",
      "val 损失: 1.7143 Top-1 准确率: 0.5341 Top-5 准确率: 0.8300\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5535 Top-1 准确率: 0.5982 Top-5 准确率: 0.8449\n",
      "val 损失: 1.4930 Top-1 准确率: 0.5839 Top-5 准确率: 0.8545\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4997 Top-1 准确率: 0.6116 Top-5 准确率: 0.8548\n",
      "val 损失: 1.5231 Top-1 准确率: 0.5834 Top-5 准确率: 0.8513\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4513 Top-1 准确率: 0.6220 Top-5 准确率: 0.8607\n",
      "val 损失: 1.4672 Top-1 准确率: 0.5957 Top-5 准确率: 0.8626\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4180 Top-1 准确率: 0.6318 Top-5 准确率: 0.8658\n",
      "val 损失: 1.4835 Top-1 准确率: 0.5913 Top-5 准确率: 0.8570\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3923 Top-1 准确率: 0.6376 Top-5 准确率: 0.8743\n",
      "val 损失: 1.3916 Top-1 准确率: 0.6066 Top-5 准确率: 0.8710\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3353 Top-1 准确率: 0.6517 Top-5 准确率: 0.8811\n",
      "val 损失: 1.4120 Top-1 准确率: 0.6131 Top-5 准确率: 0.8693\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3141 Top-1 准确率: 0.6569 Top-5 准确率: 0.8845\n",
      "val 损失: 1.4081 Top-1 准确率: 0.6107 Top-5 准确率: 0.8702\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2748 Top-1 准确率: 0.6644 Top-5 准确率: 0.8915\n",
      "val 损失: 1.4385 Top-1 准确率: 0.6060 Top-5 准确率: 0.8729\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2548 Top-1 准确率: 0.6709 Top-5 准确率: 0.8953\n",
      "val 损失: 1.5142 Top-1 准确率: 0.5892 Top-5 准确率: 0.8572\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2127 Top-1 准确率: 0.6806 Top-5 准确率: 0.9014\n",
      "val 损失: 1.3229 Top-1 准确率: 0.6339 Top-5 准确率: 0.8830\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1825 Top-1 准确率: 0.6882 Top-5 准确率: 0.9061\n",
      "val 损失: 1.3339 Top-1 准确率: 0.6378 Top-5 准确率: 0.8819\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1602 Top-1 准确率: 0.6948 Top-5 准确率: 0.9091\n",
      "val 损失: 1.2878 Top-1 准确率: 0.6431 Top-5 准确率: 0.8935\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1234 Top-1 准确率: 0.7037 Top-5 准确率: 0.9163\n",
      "val 损失: 1.4360 Top-1 准确率: 0.6128 Top-5 准确率: 0.8688\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1203 Top-1 准确率: 0.7031 Top-5 准确率: 0.9180\n",
      "val 损失: 1.3418 Top-1 准确率: 0.6337 Top-5 准确率: 0.8815\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0729 Top-1 准确率: 0.7144 Top-5 准确率: 0.9225\n",
      "val 损失: 1.3231 Top-1 准确率: 0.6401 Top-5 准确率: 0.8816\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0595 Top-1 准确率: 0.7200 Top-5 准确率: 0.9244\n",
      "val 损失: 1.2385 Top-1 准确率: 0.6608 Top-5 准确率: 0.8936\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0195 Top-1 准确率: 0.7304 Top-5 准确率: 0.9299\n",
      "val 损失: 1.2854 Top-1 准确率: 0.6529 Top-5 准确率: 0.8922\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0055 Top-1 准确率: 0.7332 Top-5 准确率: 0.9331\n",
      "val 损失: 1.2472 Top-1 准确率: 0.6642 Top-5 准确率: 0.8906\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9866 Top-1 准确率: 0.7380 Top-5 准确率: 0.9358\n",
      "val 损失: 1.2824 Top-1 准确率: 0.6509 Top-5 准确率: 0.8917\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9559 Top-1 准确率: 0.7462 Top-5 准确率: 0.9412\n",
      "val 损失: 1.3322 Top-1 准确率: 0.6418 Top-5 准确率: 0.8843\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9519 Top-1 准确率: 0.7462 Top-5 准确率: 0.9425\n",
      "val 损失: 1.3550 Top-1 准确率: 0.6406 Top-5 准确率: 0.8812\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9230 Top-1 准确率: 0.7540 Top-5 准确率: 0.9461\n",
      "val 损失: 1.2323 Top-1 准确率: 0.6642 Top-5 准确率: 0.8964\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9120 Top-1 准确率: 0.7570 Top-5 准确率: 0.9480\n",
      "val 损失: 1.3240 Top-1 准确率: 0.6481 Top-5 准确率: 0.8865\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8923 Top-1 准确率: 0.7615 Top-5 准确率: 0.9520\n",
      "val 损失: 1.3009 Top-1 准确率: 0.6572 Top-5 准确率: 0.8865\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8701 Top-1 准确率: 0.7666 Top-5 准确率: 0.9545\n",
      "val 损失: 1.2047 Top-1 准确率: 0.6764 Top-5 准确率: 0.8960\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8517 Top-1 准确率: 0.7717 Top-5 准确率: 0.9556\n",
      "val 损失: 1.2100 Top-1 准确率: 0.6791 Top-5 准确率: 0.8993\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8373 Top-1 准确率: 0.7762 Top-5 准确率: 0.9580\n",
      "val 损失: 1.2745 Top-1 准确率: 0.6694 Top-5 准确率: 0.9002\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8156 Top-1 准确率: 0.7806 Top-5 准确率: 0.9606\n",
      "val 损失: 1.2693 Top-1 准确率: 0.6633 Top-5 准确率: 0.8924\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8054 Top-1 准确率: 0.7837 Top-5 准确率: 0.9626\n",
      "val 损失: 1.3145 Top-1 准确率: 0.6597 Top-5 准确率: 0.8907\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7886 Top-1 准确率: 0.7902 Top-5 准确率: 0.9639\n",
      "val 损失: 1.3343 Top-1 准确率: 0.6604 Top-5 准确率: 0.8888\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7663 Top-1 准确率: 0.7933 Top-5 准确率: 0.9673\n",
      "val 损失: 1.3346 Top-1 准确率: 0.6606 Top-5 准确率: 0.8866\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7695 Top-1 准确率: 0.7938 Top-5 准确率: 0.9676\n",
      "val 损失: 1.2369 Top-1 准确率: 0.6799 Top-5 准确率: 0.8983\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7593 Top-1 准确率: 0.7952 Top-5 准确率: 0.9701\n",
      "val 损失: 1.3341 Top-1 准确率: 0.6604 Top-5 准确率: 0.8896\n",
      "\n",
      "训练完成，耗时 42m 46s\n",
      "最佳验证集准确率: 0.679900\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 0.6019 Top-1 准确率: 0.8403 Top-5 准确率: 0.9796\n",
      "val 损失: 0.9842 Top-1 准确率: 0.7330 Top-5 准确率: 0.9272\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 0.5319 Top-1 准确率: 0.8572 Top-5 准确率: 0.9854\n",
      "val 损失: 0.9584 Top-1 准确率: 0.7445 Top-5 准确率: 0.9297\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.5172 Top-1 准确率: 0.8599 Top-5 准确率: 0.9879\n",
      "val 损失: 0.9490 Top-1 准确率: 0.7456 Top-5 准确率: 0.9291\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4992 Top-1 准确率: 0.8647 Top-5 准确率: 0.9900\n",
      "val 损失: 0.9583 Top-1 准确率: 0.7447 Top-5 准确率: 0.9302\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4839 Top-1 准确率: 0.8691 Top-5 准确率: 0.9898\n",
      "val 损失: 0.9519 Top-1 准确率: 0.7468 Top-5 准确率: 0.9294\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4878 Top-1 准确率: 0.8666 Top-5 准确率: 0.9909\n",
      "val 损失: 0.9565 Top-1 准确率: 0.7450 Top-5 准确率: 0.9303\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4640 Top-1 准确率: 0.8737 Top-5 准确率: 0.9920\n",
      "val 损失: 0.9656 Top-1 准确率: 0.7421 Top-5 准确率: 0.9292\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4655 Top-1 准确率: 0.8720 Top-5 准确率: 0.9919\n",
      "val 损失: 0.9429 Top-1 准确率: 0.7482 Top-5 准确率: 0.9309\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4627 Top-1 准确率: 0.8726 Top-5 准确率: 0.9924\n",
      "val 损失: 0.9678 Top-1 准确率: 0.7417 Top-5 准确率: 0.9286\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.4573 Top-1 准确率: 0.8729 Top-5 准确率: 0.9928\n",
      "val 损失: 0.9596 Top-1 准确率: 0.7440 Top-5 准确率: 0.9294\n",
      "\n",
      "训练完成，耗时 8m 34s\n",
      "最佳验证集准确率: 0.748200\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9546 Top-1 准确率: 0.7545 Top-5 准确率: 0.9690\n",
      "val 损失: 0.9359 Top-1 准确率: 0.7464 Top-5 准确率: 0.9290\n",
      "\n",
      "第 1/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9525 Top-1 准确率: 0.7550 Top-5 准确率: 0.9719\n",
      "val 损失: 0.9363 Top-1 准确率: 0.7467 Top-5 准确率: 0.9295\n",
      "\n",
      "第 2/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9217 Top-1 准确率: 0.7626 Top-5 准确率: 0.9740\n",
      "val 损失: 0.9395 Top-1 准确率: 0.7467 Top-5 准确率: 0.9294\n",
      "\n",
      "第 3/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9126 Top-1 准确率: 0.7647 Top-5 准确率: 0.9746\n",
      "val 损失: 0.9408 Top-1 准确率: 0.7461 Top-5 准确率: 0.9285\n",
      "\n",
      "第 4/29 轮训练\n",
      "----------\n",
      "train 损失: 0.9196 Top-1 准确率: 0.7613 Top-5 准确率: 0.9766\n",
      "val 损失: 0.9449 Top-1 准确率: 0.7456 Top-5 准确率: 0.9297\n",
      "\n",
      "第 5/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8961 Top-1 准确率: 0.7682 Top-5 准确率: 0.9778\n",
      "val 损失: 0.9288 Top-1 准确率: 0.7474 Top-5 准确率: 0.9298\n",
      "\n",
      "第 6/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8941 Top-1 准确率: 0.7680 Top-5 准确率: 0.9797\n",
      "val 损失: 0.9266 Top-1 准确率: 0.7504 Top-5 准确率: 0.9309\n",
      "\n",
      "第 7/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8886 Top-1 准确率: 0.7687 Top-5 准确率: 0.9787\n",
      "val 损失: 0.9423 Top-1 准确率: 0.7449 Top-5 准确率: 0.9289\n",
      "\n",
      "第 8/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8888 Top-1 准确率: 0.7686 Top-5 准确率: 0.9810\n",
      "val 损失: 0.9418 Top-1 准确率: 0.7473 Top-5 准确率: 0.9296\n",
      "\n",
      "第 9/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8828 Top-1 准确率: 0.7700 Top-5 准确率: 0.9805\n",
      "val 损失: 0.9418 Top-1 准确率: 0.7474 Top-5 准确率: 0.9303\n",
      "\n",
      "第 10/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8748 Top-1 准确率: 0.7704 Top-5 准确率: 0.9823\n",
      "val 损失: 0.9509 Top-1 准确率: 0.7466 Top-5 准确率: 0.9291\n",
      "\n",
      "第 11/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8693 Top-1 准确率: 0.7723 Top-5 准确率: 0.9828\n",
      "val 损失: 0.9236 Top-1 准确率: 0.7550 Top-5 准确率: 0.9331\n",
      "\n",
      "第 12/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8696 Top-1 准确率: 0.7714 Top-5 准确率: 0.9833\n",
      "val 损失: 0.9516 Top-1 准确率: 0.7487 Top-5 准确率: 0.9282\n",
      "\n",
      "第 13/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8559 Top-1 准确率: 0.7758 Top-5 准确率: 0.9842\n",
      "val 损失: 0.9420 Top-1 准确率: 0.7489 Top-5 准确率: 0.9288\n",
      "\n",
      "第 14/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8558 Top-1 准确率: 0.7747 Top-5 准确率: 0.9840\n",
      "val 损失: 0.9451 Top-1 准确率: 0.7503 Top-5 准确率: 0.9300\n",
      "\n",
      "第 15/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8477 Top-1 准确率: 0.7767 Top-5 准确率: 0.9851\n",
      "val 损失: 0.9533 Top-1 准确率: 0.7479 Top-5 准确率: 0.9307\n",
      "\n",
      "第 16/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8477 Top-1 准确率: 0.7773 Top-5 准确率: 0.9856\n",
      "val 损失: 0.9480 Top-1 准确率: 0.7502 Top-5 准确率: 0.9295\n",
      "\n",
      "第 17/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8601 Top-1 准确率: 0.7729 Top-5 准确率: 0.9856\n",
      "val 损失: 0.9595 Top-1 准确率: 0.7489 Top-5 准确率: 0.9282\n",
      "\n",
      "第 18/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8432 Top-1 准确率: 0.7768 Top-5 准确率: 0.9863\n",
      "val 损失: 0.9530 Top-1 准确率: 0.7536 Top-5 准确率: 0.9293\n",
      "\n",
      "第 19/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8407 Top-1 准确率: 0.7767 Top-5 准确率: 0.9875\n",
      "val 损失: 0.9473 Top-1 准确率: 0.7507 Top-5 准确率: 0.9285\n",
      "\n",
      "第 20/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8350 Top-1 准确率: 0.7786 Top-5 准确率: 0.9867\n",
      "val 损失: 0.9454 Top-1 准确率: 0.7526 Top-5 准确率: 0.9302\n",
      "\n",
      "第 21/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8300 Top-1 准确率: 0.7806 Top-5 准确率: 0.9876\n",
      "val 损失: 0.9463 Top-1 准确率: 0.7538 Top-5 准确率: 0.9296\n",
      "\n",
      "第 22/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8294 Top-1 准确率: 0.7790 Top-5 准确率: 0.9883\n",
      "val 损失: 0.9534 Top-1 准确率: 0.7528 Top-5 准确率: 0.9279\n",
      "\n",
      "第 23/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8252 Top-1 准确率: 0.7804 Top-5 准确率: 0.9886\n",
      "val 损失: 0.9584 Top-1 准确率: 0.7525 Top-5 准确率: 0.9301\n",
      "\n",
      "第 24/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8244 Top-1 准确率: 0.7795 Top-5 准确率: 0.9889\n",
      "val 损失: 0.9577 Top-1 准确率: 0.7520 Top-5 准确率: 0.9268\n",
      "\n",
      "第 25/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8117 Top-1 准确率: 0.7844 Top-5 准确率: 0.9890\n",
      "val 损失: 0.9643 Top-1 准确率: 0.7511 Top-5 准确率: 0.9277\n",
      "\n",
      "第 26/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8115 Top-1 准确率: 0.7832 Top-5 准确率: 0.9894\n",
      "val 损失: 0.9662 Top-1 准确率: 0.7522 Top-5 准确率: 0.9298\n",
      "\n",
      "第 27/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8187 Top-1 准确率: 0.7811 Top-5 准确率: 0.9897\n",
      "val 损失: 0.9549 Top-1 准确率: 0.7548 Top-5 准确率: 0.9297\n",
      "\n",
      "第 28/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8099 Top-1 准确率: 0.7833 Top-5 准确率: 0.9901\n",
      "val 损失: 0.9763 Top-1 准确率: 0.7525 Top-5 准确率: 0.9245\n",
      "\n",
      "第 29/29 轮训练\n",
      "----------\n",
      "train 损失: 0.8080 Top-1 准确率: 0.7825 Top-5 准确率: 0.9904\n",
      "val 损失: 0.9498 Top-1 准确率: 0.7533 Top-5 准确率: 0.9285\n",
      "\n",
      "训练完成，耗时 19m 53s\n",
      "最佳验证集准确率: 0.755000\n"
     ]
    }
   ],
   "source": [
    "model3,(train_losses3, train_top1_accs3, train_top5_accs3, val_losses3, val_top1_accs3, val_top5_accs3) = train_model(model2,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model3, 'fixed90epoch_highway+02drop.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的隐藏层是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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